TY  - JOUR
AU  - Li, Joe
AU  - Washington, Peter
PY  - 2024
DA  - 2024/5/10
TI  - A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study
JO  - JMIR AI
SP  - e52171
VL  - 3
KW  - affect detection
KW  - affective computing
KW  - deep learning
KW  - digital health
KW  - emotion recognition
KW  - machine learning
KW  - mental health
KW  - personalization
KW  - stress detection
KW  - wearable technology
AB  - Background: There are a wide range of potential adverse health effects, ranging from headaches to cardiovascular disease, associated with long-term negative emotions and chronic stress. Because many indicators of stress are imperceptible to observers, the early detection of stress remains a pressing medical need, as it can enable early intervention. Physiological signals offer a noninvasive method for monitoring affective states and are recorded by a growing number of commercially available wearables. Objective: We aim to study the differences between personalized and generalized machine learning models for 3-class emotion classification (neutral, stress, and amusement) using wearable biosignal data. Methods: We developed a neural network for the 3-class emotion classification problem using data from the Wearable Stress and Affect Detection (WESAD) data set, a multimodal data set with physiological signals from 15 participants. We compared the results between a participant-exclusive generalized, a participant-inclusive generalized, and a personalized deep learning model. Results: For the 3-class classification problem, our personalized model achieved an average accuracy of 95.06% and an F1-score of 91.71%; our participant-inclusive generalized model achieved an average accuracy of 66.95% and an F1-score of 42.50%; and our participant-exclusive generalized model achieved an average accuracy of 67.65% and an F1-score of 43.05%. Conclusions: Our results emphasize the need for increased research in personalized emotion recognition models given that they outperform generalized models in certain contexts. We also demonstrate that personalized machine learning models for emotion classification are viable and can achieve high performance. 
SN  - 2817-1705
UR  - https://ai.jmir.org/2024/1/e52171
UR  - https://doi.org/10.2196/52171
DO  - 10.2196/52171
ID  - info:doi/10.2196/52171
ER  -